Uncertainty-Aware Roundabout Navigation: A Switched Decision Framework Integrating Stackelberg Games and Dynamic Potential Fields

Roundabout navigation presents significant challenges for autonomous vehicles (AVs) due to complex multi-vehicle interactions, highly dynamic and uncertain traffic patterns, and the requirement to coordinate with multiple vehicles simultaneously. Existing approaches often struggle to maintain safety and efficiency in uncertain vehicle behaviors and space-constrained situations, especially for mandatory lane changes and exit maneuvers. This paper presents a switched decision system that combines game theory and potential fields to enable robust navigation in roundabouts. The proposed approach first develops a Stackelberg game framework with uncertainty-aware prediction, where AVs dynamically adjust their decisions based on probabilistic estimates of surrounding vehicles' behaviors. Additionally, a hybrid potential field method is introduced that seamlessly transitions to emergency maneuvering when standard game-theoretic solutions become infeasible. Through extensive simulations under varied scenarios, the proposed approach achieves 97% successful navigation rates while maintaining collision rates below 1%, significantly outperforming baseline Nash equilibrium methods. The results demonstrate that integrating uncertainty characterization with strategic cooperation significantly improves the overall safety and efficiency of autonomous driving in roundabouts.